Clinical impact of unclassified variants of the <i>BRCA1</i> and <i>BRCA2</i> genes
Bibliographic record
Abstract
Women who carry a pathogenic mutation in BRCA1 or BRCA2 have high risks of developing breast and ovarian cancers. The functional effect of many missense variants on BRCA1 and BRCA2 protein function is not known. Here, the authors construct a historical cohort of 4030 female first-degree relatives of 1345 unselected patients with ovarian cancer who have been screened for BRCA1 and BRCA2 mutations. The authors compared the risks by the age of 80 years for all cancers combined in female first-degree relatives of women with a pathogenic mutation, women with a variant of unknown significance (unclassified variant) and non-carriers. The cumulative risk of cancer among the relatives of patients with a pathogenic mutation was much higher than the risk in relatives of non-carriers (50.2% vs 28.5%; HR=2.87, p<10(-4)). In contrast, the cumulative risk of cancer among relatives of patients carrying an unclassified variant was similar to the risk of cancer for relatives of non-carriers (27.6% vs 28.5%; HR=1.08, p=0.79). The authors used three different algorithms to predict the pathogenicity of unclassified variants and compared their penetrance with non-carriers. In this sample, only Align Grantham Variation Grantham Deviation appeared to predict penetrance based on first-degree relatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".